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            A conversation with Professor Bin Yu
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           7月
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           2013
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          <p>
           去年就答应了
           <a href="http://www.icsa.org" target="_blank">
            泛华统计协会
           </a>
           去采访博士论文导师
           <a href="http://www.stat.berkeley.edu/~binyu" target="_blank">
            郁彬
           </a>
           ，今年二月时才借道成行，在Berkeley做了采访。
           <a href="http://www.stat.osu.edu/~taoshi/research/papers/Conversation_with_Bin_YU.pdf" target="_blank">
            采访全文
           </a>
           刚刚在
           <a href="http://www.icsa.org/bulletin/index.html" target="_blank">
            这期的 ICSA Bulletin
           </a>
           上登出。
          </p>
          <p>
           先说一句：采访比较长，而且是英文，请有耐心时再读。（注：曾经给某达人试读过关于中国统计看法的部分，刚好赶上人家情绪不好，被直接无视。）
          </p>
          <p>
           我以自己的偏好摘抄几段：
          </p>
          <div title="Page 1">
           <p>
            [Before College]
           </p>
           <blockquote>
            <p>
             A math book from a cousin gave me my first boost into math when I was in 3
             <sup>
              rd
             </sup>
             and 4
             <sup>
              th
             </sup>
             grade. I enjoyed taking exponentials and logarithms using a table in the book.  I  believe doing the math problems provided a refuge of certainty and safety for me during a very turmoil time in China.
            </p>
            <p>
             Another big boost in my interest in mathematics occurred when I was in the Lab School of Normal University in Harbin.  There I had a wonderful and extremely talented sub math teacher, Jianye  Chen (陈建业) in my second year in junior high. [……] Under his strong influence and, in some sense, fulfilling his unrealized dream of going to the math department at Peking University, I chose to do math at Peking University after receiving a very good score on the national college entrance examination in 1980.
            </p>
           </blockquote>
           <p>
            [PKU]
           </p>
           <blockquote>
            <p>
             The first math analysis discussion class was hard for me since I didn’t know how to do the problems. But you know, I really liked math and we had good professors. We didn’t interact a lot with the professors, because that was not the norm.
            </p>
            <p>
             In the entrance exam to graduate school in Peking University, I came first in the math subject exams. However, the professor I wanted to work with did not take me after the oral exam. So I switched into Probability and Statistics, although I originally wanted to do Functional Analysis. That was actually a very good move, a forced one, but it has benefited me tremendously.
            </p>
           </blockquote>
           <p>
            [Qualify Exam at Berkeley]
           </p>
           <blockquote>
            <p>
             <i>
              Shi:
             </i>
             Is it the same format as we took it? 10 questions?
            </p>
            <p>
             <i>
              Yu:
             </i>
             Yes. If you do three, I think, you pass.
            </p>
           </blockquote>
           <p>
            [Marriage]
           </p>
           <blockquote>
            <p>
             In the summer of 1987, I went back to China and got married to my boyfriend who went to graduate school in China in 1985 in architectural history. He was able to join me a year later in Berkeley and went to Berkeley’s School of Architecture. My American friends were a bit shocked to hear that I married someone that I hadn’t seen for two years. It was a bit risky, but looking back, it was the best decision in my life.
            </p>
           </blockquote>
           <p>
            [Suggestion for Young Researchers]
           </p>
           <blockquote>
            <p>
             So I would say to junior people who just started their career: take more risks, instead of being more careful. If you work in a very desirable field like Statistics, you could not go too wrong. Ultimately, whether you enjoy your life or not is because whether you are happy, not because you make the system happy. And the system actually becomes happy because you are happy.
            </p>
           </blockquote>
           <p>
            [Current Status of Statistics]
           </p>
           <blockquote>
            <p>
             I think we are in a golden area for Statistics as an intellectual field. But this field has to be broadly interpreted. Basically a lot of people trained in other fields are also doing this type of work we do.
            </p>
            <p>
             I think if we rise up to the challenge, we will be the leading data scientists. With our great traditions of critical thinking with us, at the same time, embracing machine learning, database, and computing challenges.
            </p>
            <p>
             You take some risks, and you cannot really “fail” too much. You have a safe net. You have a Ph.D. in Statistics. How wrong could it go, right?
            </p>
           </blockquote>
           <p>
            [Statistics in China]
           </p>
           <blockquote>
            <p>
             <i>
              Shi
             </i>
             : By talking with people in China, I do feel industry, especially the high-tech companies, has a huge need for people who can analyze their growing volume of data. Meanwhile, in more scientific area like Biology and Physics, they do have the same need to find people who can work with them in designing and analyzing their experiments and do better science. Is there anything universities in China can do to help foster this type of collaboration?
            </p>
            <p>
             <i>
              Yu
             </i>
             : I think it is kind of happening already. Peking University is talking about a data science center. You have to have cross discipline centers. Any culture change is going to be a slow process. But when there is a need, especially for economic reasons, things just happen in the end. The statistics majors in China, and here too, have to get on top of computing. At senior level, it is easy to find collaborators because you have ideas and a record. If you are a beginner and you cannot even touch the data, who’s going to hire a statistics undergraduate to give advice to a CS undergraduate? It is a constant struggle that we should keep up with computing training of our students. Eventually I hope we will be just as good as computer science majors. That would be the goal, then we will have both the critical thinking and computing skills. I’m not worried about the mathematical part as much not because it is not important. We have been giving our students that, so it is not the urgent need.  The weaker point is the cross-field critical thinking and computing for statistics students.
            </p>
           </blockquote>
           <p>
            [Statistics and Data Science]
           </p>
           <blockquote>
            <p>
             Yu: [……] Lots of people think of statistics as counting numbers, but they don’t know all the exciting things we do. That’s a misconception. Either we go all the way out as a community to change it, which is an uphill battle, or we just embrace data science. Just start saying that we do data science. It is psychology. This is a personal opinion, not representing the view of IMS. I’m just wondering and I think it is a discussion worth having because of the popular unfavorable misconception of statistics.
            </p>
            <p>
             <i>
              Shi
             </i>
             : Yes. I have colleagues who seldom read the Annuals of Statistics. They think the journal mainly concerns about theoretical results and mainly about asymptotic, but they are not.
            </p>
            <p>
             <i>
              Yu
             </i>
             : It is a dilemma in China. Statistics (统计) is 一级学科. Data science is not one of the 学科 yet. But in certain occasions, we can say that we do data science. We are statisticians and we do data science. At least we should go that far.
            </p>
           </blockquote>
           <p>
            [Statistics and Critical Thinking]
           </p>
           <blockquote>
            <p>
             <i>
              Yu
             </i>
             : That’s a gradual process. As I feel being the chair is confronting different opinions. As you said, you cannot form critical thinking without people counter you, even just playing the devil’s advocate. If it is all “great”, it is not critical thinking. Critical thinking is not the most natural thing in the Chinese culture because we tend to want to agree with each other, which has strength in lots of situations, but not in Science. It is something I think the western culture has an edge. In the Chinese culture, there are things called “思辨”and “承传”, but it is more about listening to others than questioning.
            </p>
            <p>
             I’m not disapproving by critiquing, but some students might take that way. So the challenge to me is how to train those students to become critical thinkers. It is almost like they have to establish confidence first somehow.
            </p>
           </blockquote>
           <p>
            [Data Collection and Quality in China]
           </p>
           <blockquote>
            <p>
             <i>
              Shi
             </i>
             : I found it amazing to see on the Internet that comments about any data or any article written by Bureau of Statistics of China are usually like people don’t trust any of them. It seems don’t matter what the report is about. When it says something is good, they don’t trust it; when it says something is bad, they don’t trust it.
            </p>
            <p>
             <i>
              Yu
             </i>
             : Yeah, that’s a big problem you bring up that is data quality. It is not unrelated to plagiarism in doing research at every level. For statistics, if we cannot trust the data, we are done. Maybe theoretical statistics will develop further first before data analysis or data science. But companies care a lot more about good quality of data. They cannot fake their data as much because it is related with their revenue. That’s why I say industry would play a huge role in pushing the development of statistics or data science, whatever it is called, in China.
            </p>
           </blockquote>
           <p>
            我知道，我知道，光摘要就这么长。。。 要是你已经读到这了，那，
           </p>
           <p>
            恭喜你！你的耐心和对统计学科（数据科学）的兴趣足以支持你读
            <a href="http://www.stat.osu.edu/~taoshi/research/papers/Conversation_with_Bin_YU.pdf" target="_blank">
             完全部访谈
            </a>
            的。
           </p>
           <p>
            我保证你会有不同的收获（最少可以看到彬姐在足球队里的风采）。
           </p>
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            2011年十一月
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           奥林匹克
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           情绪预测
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           数据挖掘
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           欧洲大型强子对撞机
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           男女比例
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           留学申请
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           精算师
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           统计
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